Vehicular information systems and methods

ABSTRACT

Disclosed is a method and system that receives sensor information from each of a plurality of sensors. Each sensor in the plurality is associated with a vehicle. The sensor information includes location coordinates of each vehicle in the plurality. The sensor information associated with each vehicle in the plurality then is translated to parking statistics information. In one embodiment, the translation is based on an aggregate of sensor information corresponding to the plurality of vehicles. The system then communicates parking statistics information to the vehicle.

RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional Application Ser.No. 61/177,710, filed May 13, 2009 which is incorporated herein byreference in its entirety.

FIELD

The present disclosure relates to vehicles, and more specifically to amethod and system for obtaining and communicating vehicular information.

BACKGROUND

Parking space availability is a major problem in crowded areas,particularly urban areas. The importance of better parking systems inurban areas has been recognized recently as one of the most importantavenues for betterment of urban infrastructure. One study estimated aloss of $78 billion in one year in the form of 4.2 billion lost hoursand 2.9 billion gallons of wasted gasoline in the United States alone.Several projects recently have sought to address this issue through thedesign of mobile systems that collect traffic congestion information toimprove route finding and trip planning. Unfortunately, a significantportion of traffic congestion and travel delays are experienced indowntown areas where it is not always possible to reroute a driver. Inthese densely populated urban areas, congestion and travel delays alsoare due to parking. In one study, researchers found in one smallbusiness district of Los Angeles that, over the course of a year,vehicles looking for parking created the equivalent of 38 trips aroundthe world, burning 47,000 gallons of gasoline and producing 730 tons ofcarbon dioxide. Clearly, addressing the problems associated with parkingin downtown areas would have significant societal impact, botheconomically and ecologically.

Prior attempts to solve this problem often have focused on monitoringthe presence or absence of a vehicle over each parking spot using adedicated sensor. These attempts typically rely on fixed sensorsinstalled by municipalities in the ground or on parking meters. Thisresults in a large fixed cost for installation and operation in order tocover parking spaces at a city-wide level (e.g., millions of dollars tocover a small percentage of the total number of parking spots).

As a result, there remains a need to better address problems associatedwith parking space availability.

SUMMARY

Addressing this problem does not necessarily require real-timeidentification of individual available parking spots. Instead there isalso great value in collecting approximate parking statistics, forexample aggregate counts of available parking spots on one road orhistorical averages of parking spot usage.

For example, such spatio-temporal statistics on parking availability istypically valuable to municipal governments to make better decisionsabout how to set prices for street-parking, setting time-limits, andwhere to install parking meters. Beyond adjusting road-side parkingprices, detailed parking availability statistics could be widelydisseminated on web-based maps or navigation systems which would incurthe following further benefits:

(i) Improve traveler decisions, with respect to mode of transportation,the choice of road-side parking vs. parking garage, and in which area tosearch for road-side parking,

(ii) Allow parking garages to adjust their prices dynamically to respondto the availability or non-availability of parking spaces in theimmediate area, and

(iii) Improve efficiency of parking enforcement by directing enforcementresources to areas where violations are most likely to incur.

In one aspect, a vehicular information system and method includesreceiving, by a server computer, sensor information from each of aplurality of sensors. Each sensor in the plurality is associated with avehicle. The sensor information includes location coordinates of eachvehicle in the plurality. The server computer translates the sensorinformation associated with each vehicle in the plurality to parkingstatistics information (e.g., counts of spaces per road, fraction ofroadway available for parking, historical averages for a road, etc.). Inone embodiment, the translation is based on an aggregate of sensorinformation corresponding to the plurality of vehicles. In oneembodiment, the server computer communicates the parking statisticsinformation to the vehicle.

In one embodiment, the receiving of the sensor information includesreceiving a range of the sensor and/or receiving a speed of the sensor.In one embodiment, the translating includes determining if each vehiclein the plurality is in a slotted parking area or in an unslotted parkingarea. If a vehicle is in the slotted parking area, the sensorinformation is translated to parking space counts. If a vehicle is inthe unslotted parking area, the sensor information is translated to aparking space map. In one embodiment, the receiving of sensorinformation includes receiving video from a camera (e.g., a webcam)associated with each sensor in the plurality. The video can include aplurality of images that are time stamped.

In one embodiment, the receiving further includes determining that thelocation coordinates fall within a range of location coordinatesassociated with the start of the receiving. Stopping of the receivingstep can occur when the location coordinates fall outside of the rangeof location coordinates associated with the start of the receiving. Inone embodiment, the translating further includes determining the widthof a dip in sensor information. The determining of the width can includedetermining a number of vehicles to which the dip corresponds, comparingthe width to a threshold width, and/or determining the depth of a dip.

In one embodiment, the translation is based on an aggregate of sensorinformation corresponding to the plurality of vehicles. In oneembodiment, a location estimate of the vehicle is corrected by matchingtime-series sensor information (sensor information collected over aperiod of time) to sensor information from prior trips along a road. Inone embodiment, traces are aligned based on distinct signaturesgenerated by fixed road-side objects. In one embodiment, the translatingis based on continually comparing time-series sensor informationobserved by each sensor to a set of signatures known to correspond totypical vehicles. In one embodiment, the translating further includescalculating the parking statistics information over a predeterminedperiod of time (e.g., average parking availability in a city block on aSaturday afternoon over the past month). In one embodiment, the parkingstatistics information calculated over the predetermined period of timeis used to predict availability of parking (e.g., future availability orcurrent availability).

These and other aspects and embodiments will be apparent to those ofordinary skill in the art by reference to the following detaileddescription and the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

In the drawing figures, which are not to scale, and where like referencenumerals indicate like elements throughout the several views:

FIG. 1 is a block diagram of a vehicle having a sensor fitted on thevehicle in accordance with an embodiment of the present disclosure;

FIG. 2A is a block diagram of a vehicle having a sensor and a computingdevice communicating with a server over a network in accordance with anembodiment of the present disclosure;

FIG. 2B is a block diagram of a sensed scene, the parking statisticsinformation output, and applications of the output in accordance with anembodiment of the present disclosure;

FIG. 3 is a flowchart illustrating the steps performed by the server ofFIG. 2A in accordance with an embodiment of the present disclosure;

FIG. 4 is a block diagram of the sensor information obtained by a sensorof a vehicle being transmitted via a wireless service in accordance withan embodiment of the present disclosure;

FIG. 5 shows parking regions to which data collection is limited inaccordance with an embodiment of the present disclosure;

FIGS. 6A and 6B shows examples of a trace produced by the sensor as asensing vehicle drives past two parked cars in accordance with anembodiment of the present disclosure;

FIG. 7 is a flowchart illustrating filtering stages applied to eachdetected dip of a sensor reading in a trace in accordance with anembodiment of the present disclosure;

FIG. 8 is a plot of a depth and width of peaks observed in 19 separatetrips in an area with slotted parking in accordance with an embodimentof the present disclosure;

FIG. 9 is a plot showing the sensor reading and ground truth, speed, andoutput of the detection algorithm in accordance with an embodiment ofthe present disclosure;

FIG. 10A is a plot illustrating the tradeoff between detection rate andfalse positives for a slotted model, as the threshold for the width of adip is varied in accordance with an embodiment of the presentdisclosure;

FIG. 10B is a plot of the number of detected parked vehicles on a roadwith 57 parking slots, against the true number of parked cars inaccordance with an embodiment of the present disclosure;

FIG. 11A shows a scatterplot indicating a comparison for an unslottedmodel between the estimate of space between two successive cars with thetrue value as computed using a ground truth in accordance with anembodiment of the present disclosure;

FIG. 11B shows a plot indicating the trade-off between correspondingfalse positive rate and missed detection rate as the threshold for thewidth of a dip is varied in accordance with an embodiment of the presentdisclosure;

FIG. 12 is a plot of the locations of eight objects along a street inaccordance with an embodiment of the present disclosure;

FIG. 13 shows a plot of the correlation between the error in the X and Ydirections as a function of distance along the street, using the eightobjects selected in accordance with an embodiment of the presentdisclosure;

FIG. 14 is a plot illustrating the use of the first object of FIG. 12 tocorrect the error of the remaining seven objects in accordance with anembodiment of the present disclosure;

FIG. 15 is a plot of the error-performance of the slot-matchingalgorithm when using plain uncorrected traces and with traces that havebeen corrected using the fingerprinting algorithm in accordance with anembodiment of the present disclosure;

FIG. 16A shows a raw trace of range as reported by the sensor as afunction of distance moved along a road segment in accordance with anembodiment of the present disclosure;

FIG. 16B shows a windowed variance on the raw trace of FIG. 16A inaccordance with an embodiment of the present disclosure; and

FIG. 17 is a block diagram illustrating an internal architecture of anexample of a computing device, such as the server and/or computingdevice of FIG. 2, in accordance with an embodiment of the presentdisclosure.

DESCRIPTION OF EMBODIMENTS

Embodiments are now discussed in more detail referring to the drawingsthat accompany the present application. In the accompanying drawings,like and/or corresponding elements are referred to by like referencenumbers.

Various embodiments are disclosed herein; however, it is to beunderstood that the disclosed embodiments are merely illustrative of thedisclosure that can be embodied in various forms. In addition, each ofthe examples given in connection with the various embodiments isintended to be illustrative, and not restrictive. Further, the figuresare not necessarily to scale, and some features may be exaggerated toshow details of particular components (and any size, material andsimilar details shown in the figures are intended to be illustrative andnot restrictive). Therefore, specific structural and functional detailsdisclosed herein are not to be interpreted as limiting, but merely as arepresentative basis for teaching one skilled in the art to variouslyemploy the disclosed embodiments.

FIG. 1 is a block diagram of an embodiment of a vehicle 105 having asensor 110 fitted on the vehicle 105. In one embodiment, the sensor 110transmits sensor signals 115 as the vehicle drives along a lane 120 of aroad. Next to the lane 120, there are two parked cars 130, 140 separatedby a vacant spot 150. As the vehicle 105 drives, the sensor 110transmits the sensor signals 115 and can receive one or more reflectedsensor signals (e.g., reflected sensor signals 155, 160). For example,the sensor 110 receives a reflected sensor signal 155, 160 after thesensor signal 115 reflects off of one of the parked cars 130, 140. Thepresence of a reflected sensor signal 155, 160 as well as the timedifference between when the sensor signal 115 is transmitted versus whenthe reflected sensor signal 155, 160 is received, affects thedetermination as to whether there is a vacant spot and the location ofthe vacant spot.

In one embodiment, the sensor 110 is an ultrasonic rangefinder and thevehicle 105 includes a Global Positioning System (GPS) (e.g., a Garmin18-5 Hz GPS with 12 channel receiver). These are used to monitorroad-side parking availability.

Referring to FIG. 2A, in one embodiment, the sensor 110 associated withthe vehicle 105 is in communication with a server computer (server) 205.The sensor 110 transmits sensor information 210 over a network 215(e.g., a wireless network) to the server 205. Sensor information 210 isany information transmitted from the sensor and may include, forexample, location information associated with the vehicle 105,information associated with received sensor signals 155, 160,information associated with sensor signals 115, time periods associatedwith one or more signals 115, 155, 160, speed of the vehicle 105, lanethat the vehicle 105 is driving in, etc. In one embodiment, the server205 stores the sensor information 210 in a server storage 220, such as amemory (e.g., RAM, ROM, etc.), hard drive, database, etc. In oneembodiment, the server 205 analyzes the sensor information 210 andtransmits parking statistics information 225 to the sensor 110 and/orvehicle 105. In one embodiment, the parking statistics information 225is, for example, counts of spaces per road, fraction of roadwayavailable for parking, historical averages for a road, available parkingspots, etc. The vehicle 105 can then notify the driver or passenger of avacant parking spot nearby. This notification can be a sound, spokenwords, an indication on the vehicle's navigation system screen, on thevehicle's dashboard, etc.

In one embodiment, the vehicle 105 also includes a computing device 230in communication with the sensor 110. The communication between thesensor 110 and the computing device 230 is represented by arrow 235. Thecomputing device 230 may be internal to or external from the vehicle105. For purposes of this disclosure (and as described in more detailbelow with respect to FIG. 16), a computer or computing device, such asthe server 205 or computing device 230 within the vehicle 105, includesa processor and memory for storing and executing program code, data andsoftware, which also may be tangibly stored or read from any type orvariety of well known computer readable storage media, such as magneticor optical discs or RAM-discs or tape, by way of non-limiting example.Computers can be provided with operating systems that allow theexecution of software applications in order to manipulate data. Personalcomputers, personal digital assistants (PDAs), wireless devices,cellular telephones, internet appliances, media players, home theatersystems, servers, and media centers are several non-limiting examples ofcomputing devices. The computing device 230 and/or server 205 can, inone embodiment, also include a display, such as a screen or monitor.

FIG. 2B shows a block diagram of a sensed scene 250, parking statisticsinformation output 225, and applications 255 of the output 225. Todefine concrete parking metrics, it is helpful to distinguish areaswhere vehicles are arranged in slots with demarcated parking bays (oftenseparated by lines marked on the road), which are referred to herein asslotted areas (e.g., slotted parking 260), from areas without any markedparking spots, which are referred to herein as unslotted (e.g.,unslotted parking 265). Slotted parking spaces 260 typically are usedwhere parking meters or other parking pay stations are installed. Insuch areas, it typically is easier to measure the number of availableparking spaces, because the spacing between cars is regulated. Vehiclessuch as vehicles 270, 275 drive along the road and transmit the sensorinformation 210 to the server 205. In one embodiment, the server 205aggregates the data from different vehicles 270, 275 and outputs aparking space map 280 and/or parking space counts 285. In oneembodiment, the server 205 then enables the user to access thisinformation online via a display 286 of a computer (e.g., when the useris at home), may transmit this information to a navigation device 288,may communicate the information via the radio or television, or maytransmit this information to designated parking enforcement vehicles290.

FIG. 3 is a flowchart illustrating an embodiment of the steps performedby the vehicle 105 and server 205 in conjunction with the sensor 110 todetect available parking spots. As stated above, the sensor 110transmits a sensor signal 115 (step 305) and receives a reflected sensorsignal 155, 160 (step 310). In one embodiment, the vehicle 105determines if it has received a predetermined number of reflected sensorsignals in step 315. If not, the vehicle 105 returns to step 305 (orstep 310) and transmits (or receives) another sensor signal 115(reflected sensor signal). If so, the vehicle 105 transmits the sensorinformation 210 to the server 205 (step 320). The server 205 aggregatesthe sensor information 210 for a plurality of vehicles and translates,based on this aggregation of sensor information 210, the sensorinformation associated with each vehicle in the plurality to parkingstatistics information.

In one embodiment, the server 205 determines from the sensor information210 (e.g., GPS coordinates) if the vehicle 105 is in a slotted parkingarea or an unslotted parking area (step 325). If the vehicle 105 is in aslotted parking area, the server 105 translates the sensor information210 to parking space counts (step 330). If the vehicle 105 is in anunslotted parking area, the server 105 translates the sensor information210 to a parking space map (step 335). The server 205 then communicatesthe corresponding parking statistics information 225 to the vehicle 105(step 340).

Referring to FIG. 4, several sensor-equipped vehicles transmit theirsensor readings 405 (sensor information 210) to server 205. A trace 408of sensor readings for a vehicle 105 is shown. In one embodiment, thetrace 408 includes GPS readings and timer readings. In one embodiment,the sensor readings are transmitted to the server 205 via a wirelessservice 420. In one embodiment, the server 205 combines this sensorinformation 210 with information from a parking spot map 422 to createan estimate of road-side parking availability 425. In one embodiment,the parking spot map 422 may be available in different levels of detail.Vehicles can report their data 405 over a cellular uplink butopportunistic use of Wifi connections also is possible depending oncost/delay tradeoffs. The parking availability information then can bedistributed, such as, for example, to navigation systems or distributedover the Internet. In one embodiment, the parking spot map 422 isconstructed manually from satellite imagery. In another embodiment, theparking spot map 422 is generated automatically through aggregation ofsensor data over time periods of weeks to months. For example, spacesthat almost never have cars parked are likely to be invalid parkingspaces (e.g., driveways, storefronts, illegal parking spots such asfire-hydrants, etc., or portions where parking is not allowed), whilespaces that always have a car parked are very likely not parked cars,but some other immovable object.

In one embodiment, each sensor vehicle 105 carries a passenger-sidefacing ultrasonic rangefinder to detect the presence or absence ofparked vehicles. In one embodiment, its range is equal to at least halfthe width of urban roads and the sampling rate is high enough to provideseveral samples over the length of a car at maximum city speeds. In oneembodiment, the ultrasonic rangefinder is magnetically mounted to theside of the vehicle 105.

In one embodiment, the ultrasonic rangefinder is a Maxbotix WR1waterproof rangefinder. This sensor emits sound waves every 50 ms at afrequency of 42 KHz. The sensor provides a single range reading from 12inches to 255 inches every cycle, which corresponds to the distance tothe nearest obstacle or the maximum range of 255 inches if no obstacleis detected. In one embodiment, the sensor measurements at each vehicle105 are time-stamped and location-stamped with inputs from a 5 Hz GPSreceiver, producing the following sensor information 210:

-   -   <Kernel-time, range, latitude, longitude, speed>

In one embodiment, vehicles transmit a collection of these measurements(a trace 408) to the server 205. The server 205 continuously aggregatesand processes this sensor information using probabilistic detectionalgorithms. Other sensor options also exist.

In one embodiment, to obtain ground truth information (to determine whatis located on the ground at a parking spot) for system evaluationpurposes and to be able to analyze erroneous readings, a webcam (e.g., aSony Playstation 3 Eye webcam) is integrated into the passenger-sidesensor mount. In one embodiment, to avoid angular and shift errors withrespect to the sensor, the camera is mounted just above the sensor andits orientation is aligned to the sensor. In one embodiment, theassociated program captures about 20 frames per second (fps) and tagseach image with a kernel time stamp. This time stamp links images to thesensor records obtained at approximately the same time. Each image thenis inspected (e.g., manually) and the ground truth sensor data isentered. This can facilitate the determining of the estimated aiming ofthe ultrasound sensor for error checking purposes.

FIG. 5 shows an embodiment of parking regions 500 for data collection.These parking regions were selected to use as a sample due to therelatively small areas of roadside parking on one commute trip and thelarge volume of video data involved. In one embodiment, the activationand deactivation of data collection is implemented in the system byusing the idea of a tripbox. Tripboxes represent rectangular areasdefined by two (latitude, longitude) points. Each tripbox also isassociated with an entry and an exit function, which starts and stopsdata collection, respectively. In one embodiment, the tripbox daemonreads the current GPS coordinates from the GPS receiver and checkswhether they fall inside or outside the tripbox region. If the currentcoordinates are the first instance of the mobile node inside thetripbox, it triggers the entry function. In case the mobile node isalready inside the tripbox and the next received coordinates are outsidethis region, it triggers the exit function. In one embodiment, tripboxesare used because they simplify the handling of vehicle routes, whichmight enter a parking zone from an unexpected direction, or theacquisition of a GPS fix while already inside a trip box.

In one embodiment, since GPS coordinates can oscillate due topositioning errors, the tripbox implementation can include a guarddistance and a guard time to avoid repeatedly triggering the sametripbox functions. The guard distance is a minimum distance that must betraveled between two tripbox boundary crossings. Similarly, the guardtime is the minimum time that must be spent before the next tripboxfunction can be triggered. This avoids triggering the start and the stopfunctions repeatedly due to GPS errors.

In one embodiment, the server 205 (or, in another embodiment, thevehicle 105) then executes a detection algorithm to detect availableparking spots. The detection algorithm translates the ultrasounddistance-reading trace into a count of available parking spaces. Thedistance-reading trace provides a one-dimensional view of the distanceto the nearest obstacle as the sensing vehicle 105 moves forward. FIG.6A shows an example of the trace produced by the sensor 110 as a sensingvehicle 105 drives past two parked cars. In one embodiment, the width ofa dip 605, 610 is representative of the length of a parked car, althoughthe errors in location estimates obtained from a GPS receiver candistort the true length of the car in a somewhat random manner. In oneembodiment and as described in more detail below, it is assumed thatmaps of areas with street-parking slots are available from a secondsource.

An ultrasonic sensor does not have a perfectly narrow beam-width, butinstead the beam width of the sound waves emitted widens with distance.This implies that the sensor receives echos, not just from objects thatare directly in front, but also from objects that are at an angle. Thiscan affect how the sensor 110 perceives vehicles that are parked veryclose to each another. Instead of clearly sensing the gap between thesevehicles, the “dips” in the sensor reading can become merged, asdepicted in dips 620, 625 of FIG. 6B. Still, classification of thespatial width of the dip allows one to determine the number of cars towhich a dip corresponds.

The inaccuracy of latitude and longitude values obtained from the GPSunit adds another challenge to the detection problem. The locationestimate provided by a commercial grade GPS receiver suffers from wellknown errors. Without a priori knowledge of how the GPS error varies inspace and time, it is possible that GPS errors can make a parked carappear to be shorter or longer than its true length. Since the detectionof parked vehicles depends upon distinguishing objects that are aboutthe length of a car from other, smaller obstacles in the sensor's path(such as trees, recycle bins, people, etc.), the detection sometimesleads to false alarms (i.e., dips caused by objects other than cars tobe classified as parked cars), and missed detections (i.e., parkedvehicles to be classified as something other than a parked car).

With respect to the detection algorithms, in one embodiment a slottedmodel exists and an unslotted model exists.

Slotted model: Each dip in the sensor trace has a depth and a width thatcorrespond to the distance from the sensor 110 to the object causing thedip, and the size of the object in the direction of motion of thesensing vehicle 105. The sensor trace first is pre-processed to removeall dips that have too few readings (less than 6 sensor readings,assuming a maximum speed of 37 mph and a car length of 5 meters) andcould not possible have arisen from a parked car. To detect a parkedcar, in one embodiment the width and depth of each dip in the sensorreading is compared against thresholds. These thresholds can bedetermined using training data.

In one embodiment, training data refers to a recording of spatial widthand depths of dips produced by vehicles. In one embodiment, trainingdata is collected using the webcam, which allows the visualdetermination of whether the sensor is pointed at a vehicle at any givenpoint in time and thereby enables association of a given dip with anactual parked vehicle.

FIG. 7 shows a series of filtering stages that are applied to eachdetected dip in the sensor reading. In particular, there is a trainingstage 705 that is applied to a depth based filter 710. The depth basedfilter 710 and the training stage 705 is applied to a width classifier(unslotted model) 715 and a width classifier (slotted model) 720. Thewidth classifier (unslotted model) 715 outputs the location and lengthof vacant spaces. The width classifier (slotted model) 720 outputs anumber of cars or no car. In one embodiment, 20% of the data is used totrain the model and the remaining 80% of the data is used to evaluateits performance.

FIG. 8 shows an embodiment of a plot 800 of the depth and width of thepeaks observed in 19 separate trips in an area with slotted parking. Inparticular, plot 800 is a plot of the depth and width of most prominentdips observed in the sensor reading, caused by parked cars (squares) andobjects other than cars (stars). This data set is used for training themodel used for classifying the rest of the data. In one embodiment, thisdata is used jointly for picking thresholds for the depth and width of asensor-reading dip that provide the minimum overall error rate (e.g.,the sum of the false positive rate and the miss detection rate). In oneembodiment, these thresholds were determined to be 89.7 inches for thedepth and 2.52 meters for the width, resulting in an overall error rateof 12.4%.

In one embodiment, all remaining dips are checked for spatial width, andcompared against a threshold representing the typical length of a car.For this, the interpolated GPS coordinates belonging to the starting andending sample of the dip are converted to UTM (meters) and the distancein meters between the starting and ending sample is computed. Since somedips correspond to multiple cars parked very close together, in oneembodiment, dips of a width greater than twice the threshold for one carare classified to belong to two cars, and so on. This allows thecounting of the number of cars on a stretch of road. Subtracting thisfrom the total number of slots on the road, as given by the map,provides an estimate of the number of vacant spaces.

Unslotted model: For the unslotted parking model, the number of carsthat can be accommodated on a given stretch of road depends upon themanner in which cars are parked on it at any given instant of time.Since each successive pair of parked cars in this model can have avariable amount of space between them, in one embodiment, the spacebetween successive parked cars is estimated to determine whether thespace is large enough to accommodate one or more cars. To accomplishthis, in one embodiment, the sensor trace is used to estimate thespatial distance between dips that have been classified as parked cars.The estimated length of the vacant stretch then is compared against thelength of a standard parking space (which, in one embodiment, isassigned a value of 6 meters).

In one embodiment, slotted and unslotted street-parking models arehandled separately. Further, in one embodiment, it is assumed that it iseasy to obtain information about which streets have which type ofparking as prior knowledge. For the slotted model, detecting how many ofthe parking spaces on a road segment are vacant is of interest.

For example, it is assumed that a street segment with the slottedparking model is known to have N parking slots and that at a giveninstant of time, n of these slots are vacant. A sensing vehicle thatdrives through this street determines that n̂ of the slots are vacant.The value of n̂ can differ from n due to missed detections as well asfalse positives. In one embodiment, the missed detection rate (pm) is ofinterest, i.e. the probability that a parked car is not detected.Further, the false positive rate (pf) is also of interest, i.e. theprobability that there is no parked car in a given slot but thedetection algorithm detects one. The ratio n̂/n captures the performanceof the detection algorithm in estimating the number of vacant spaces.This ratio can be smaller or larger than 1, for a given run, dependingon whether there are a greater number of missed detections or falsepositives. In one embodiment, since the thresholds for dipclassification are chosen from the training data to minimize the overallerror rate, and this is known to occur when the probability of falsealarm equals the missed detection probability, it is expected that theratio n″/n has a mean close to 1.

For the unslotted model, in one embodiment, the appropriate metric ofinterest is: ‘How many more cars can be accommodated on a given roadsegment, given the cars that are presently parked on it?’. As describedabove, estimating this number uses estimation of the space betweenparked cars. As in the slotted parking model, it is assumed that thelocations of stretches where unslotted parking spaces are available isknown and the detection algorithm is executed over such stretches.Whenever the detection algorithm ascertains that a space between twoparked cars is large enough to accommodate another car, it records theestimated space d̂. Suppose the actual space between the cars is d, thend̂ can be larger or smaller than d and, as before, the measure ofaccuracy is taken to be d̂/d. Further, the miss detection rate p_(m), isof interest, i.e. the probability that the algorithm decides that thereisn't enough space for a single car, when there actually is, and thefalse positive rate p f, is also of interest, i.e. the probability thatthe detection algorithm declares that one or more cars can beaccommodated in a space between two parked cars, whereas in realitythere is not enough space for a single car. In one embodiment, it isassumed that a vehicle of length 5 meters and at least half meter oneither side for parking, for a minimum of 6 meters, qualifies for aparking space.

In one embodiment, to evaluate the detection algorithm, the imagesrecorded by the webcam are utilized. Since the webcam records images ata rate of 21 frames per second, it matches the rate at which sensorreadings are recorded fairly well. Each image is labeled manually basedon whether the center of the image has a car in front or not. The timestamp associated with each image allows the interpolation of a locationstamp for each image. This provides the ground truth for the trainingdata set and the evaluation data set. FIG. 9 is an exemplary plot 900showing the sensor reading (dotted line) and ground truth (dashed line,high=car, low=no car), speed (increased in magnitude by ×10 for visualclarity), and the output of the detection algorithm (squares).

FIG. 10A shows a plot 1000 illustrating the tradeoff between detectionrate and false positives for the slotted model, as the threshold for thewidth of a dip (i.e. corresponding to the length of a car) is varied. Inone embodiment, a threshold of 2.5 meters provides the best tradeoff inthe minimum probability of error sense. FIG. 10B shows a plot 1010 ofthe number of detected parked vehicles on a road with 57 parking slots,against the true number of parked cars. On average, the ratio of theestimated number of cars to the true number of cars is approximately1.036, indicating a fairly good estimator of the availability of freespaces.

For the unslotted model, the estimate of space between two successivecars is compared with the true value as computed using the ground truthgenerated by the tagged video images. FIG. 11A shows this comparison asa scatterplot 1100. The estimated space is on average 96% of the truespace. Further, the estimated space is compared with the length of atypical parking slot (usually about 6 meters) to determine whether anadditional car can be accommodated. The result of this detection leadsto false positives and missed opportunities, and the trade-off betweenthe corresponding false positive rate and missed detection rate is shownin a plot 1110 of FIG. 11B, as the threshold for the width of a dip isvaried.

While the counting of available parking spaces does not require highabsolute position accuracy, creating an occupancy map of parkingincreases accuracy requirements since a detected car has to be matchedto a spot on a reference map. In one embodiment, the locationcoordinates provided by a GPS receiver are typically accurate to 3 m(standard deviation) when the Wide Area Augmentation System (WAAS)service is available. Given a parking spot length of about 7 m, one canexpect a significant rate of errors—any error greater than 3.5 m couldlead to matching a vehicle to an incorrect adjacent spot.

To address the occupancy map challenge, an occupancy map creationalgorithm is used that exploits both patterns in the sequence of parkingspots, as well as an Environmental GPS position correction method, toimprove location accuracy with respect to the parking spot map. In oneembodiment, the error in GPS coordinates is studied based on how itbehaves as a function of distance. The positioning accuracy of a GPSreceiver is affected by several factors, including ionospheric effects,satellite orbit shifts, clock errors, and multipath. Ionospheric effectstypically dominate the other error sources, except for errors thatexperience satellite occlusion (e.g., in urban canyons). Ionosphericeffects remain similar over distances of several 10 s of kilometers andthey contain significant components whose rate of change is on the orderof tens of minutes or longer. GPS errors therefore can be expected to becorrelated in time and space. However, the Wide Area Augmentation Systemwas designed to reduce these ionospheric and some other errors, raisingthe question whether the resulting GPS errors with WAAS still exhibitstrong spatio-temporal correlation.

In one embodiment, the GPS error is highly correlated at shortdistances, and the correlation tapers off with distance. Motivated bythis observation, the server (or vehicle) executes a method to improveabsolute location precision by an environmental fingerprinting approach.In particular, the sensor reading is used to detect certain fixedobjects that persistently appear in the ultrasound sensor traces, andutilize these to correct the error in the GPS trace. To validate theapproach, it is tested on the slot-matching problem described above. Itis expected that the environmental fingerprinting approach will benefitany mobile sensing application that requires precise estimates oflocation or distance between two points, as is the case in some of thescenarios in the sensing application.

In one embodiment, certain fixed objects (such as trees, recycle bins,the edges of street signs, etc., which also would be picked up by thesensor) are location-tagged in the video traces on a given street overmultiple different runs from different days. The data is tagged with thesame video tagging application developed for evaluating the detectionalgorithm. It was determined that the tagged coordinates for a givenobject from multiple runs varied significantly. Using 29 different runsand 8 objects on a street, the standard deviation of error was found tobe 4.6 m in the X-direction and 5.2 meters in the Y-direction. The errordue to variation in the lateral position of the sensing vehicle was notcorrected, because the street chosen for this was narrow enough to allowthe lateral variation to be within ±½ meter. Also this street was almostparallel to the X axis and so a larger error in the Y direction toslight variations in the sensing vehicle's lateral position was expectedto be observed.

In one embodiment, the error between GPS coordinates is correlated fromone object to the next. FIG. 12 shows a plot 1200 of the locations ofthe 8 objects along the street. In one embodiment, the centroid of the29 tagged locations is chosen for each object as the reference locationand each tagged location coordinate is subtracted to compute the error.FIG. 13 shows a plot 1300 of the correlation between the error in the Xand Y directions as a function of distance along the street, using the 8objects selected. In one embodiment, the correlation in the error isfairly high for a distance of up to 250 meters.

The above investigation suggests that if the GPS error is corrected at agiven point, then it is likely to remain corrected for an appreciabledistance. In plot 1400 of FIG. 14, the location-stamp of the firstobject on the street (lower left corner in FIG. 12) is used to correctthe errors in the location of the remaining 7 objects. As FIG. 14illustrates, the residual error in the error-corrected location-stampfor the 7 objects increases with increasing distance from object 1.

Fingerprinting the environment by relying on features in the sensortrace that are produced by fixed objects in the environment provides apossible means to improve location accuracy beyond that provided by GPSalone. However, fingerprinting a street requires multiple traces fromthat street, from which the locations of objects that are likely fixedcan be determined.

Estimating the GPS error using the sensor trace involves a taskcomparing the reported location of the pattern (dips) produced by aseries of fixed objects to the a priori known location of this pattern(as determined from multiple previous traces from the same roadsegment). The offset between the two gives an estimate of the error inthe reported location.

For example, to detect the dips corresponding to two successive fixedobjects from an experimental trace, a set of candidate dips isidentified for each object from the dips that are not classified asvehicles. Each candidate set consists of dips within a radius of 20meters of the known mean location of the fixed object (mean computedfrom past traces). One dip then is selected from each candidate set sothat the distance between the successive selected dips best matches theknown distance between the mean locations of the objects to which theycorrespond. The vector offset between the known locations and thereported locations of the objects is the GPS error estimate. Thecorrection procedure is repeated with another set of objects once thevehicle travel distance has exceeded the correlation distance.

For m such objects, i=1, . . . m, the location stamps li(x, y) of thedips corresponding to each object is recorded. These then are subtractedfrom the known true location of the object ti(x, y) (assuming thecentroid of the 29 locations as above), giving an estimate of the errorvector ei(x, y)=ti (x, y)−li(x, y). Next, this error vector from a givenobject is added to the location estimates of detected cars that aredetected to be within 100 meters of this object.

Motivated by the observation of correlation between GPS error in space,the specific application of matching detected parked cars to theirrespective slots on a street with slotted parking has been observed. Toaccomplish this, the output of the algorithm for detecting cars in theslotted model (see FIG. 7) was augmented with the estimated location ofeach detected car. In one embodiment, the locations of 57 slots on astreet were determined using a satellite picture from Google Earth. Thematching of cars to slots is an instance of the assignment problem onbipartite graphs and can be solved efficiently using the Hungarianalgorithm (a combinatorial optimization algorithm which solves anassignment problem in polynomial time). The problem involves assigningeach detected parked car with specified location coordinates in the setof detected cars, to a valid slot from among the set of 57 slotsavailable. The criterion for the assignment is the minimization of thecumulative distance between each car and its assigned slot. In oneembodiment, a MATLAB implementation of the Hungarian algorithm is usedto solve for the slot-matching of detected parked cars.

FIG. 15 shows a plot 1500 of the error-performance of the slot-matchingalgorithm when using plain uncorrected traces and with traces that havebeen corrected using the fingerprinting algorithm described above. Inone embodiment, the fingerprinting approach described in the previoussection significantly lowers the error rate in slot assignments.

In one embodiment, the power source for the in-car nodes is a powerinverter used to convert the 12 volt DC vehicle power supply to AC powersuitable for a standard PC power supply. In another embodiment, DC to DCpower supplies are installed in each car node and they are connecteddirectly to the fuse box.

In one embodiment, moving vehicles (e.g., in a different lane than thesensing vehicle) can be distinguished from parked vehicles by the lengthof sensor dips. A car moving at similar speeds as the sensing vehicle,for example, generates a very long dip. In another embodiment, a sensorwith a much larger range can greatly help lane detection.

In one embodiment, the server 205 displays (or causes to be displayed) alist of a few best possible (e.g., closest, most likely to remainunoccupied, price per hour) parking spaces to the vehicle 105 on theroad looking for a parking space. In another embodiment, the server 205transmits to the vehicle 105 (or causes to be displayed) a grossindication of the availability of parking spaces on the streets in anurban area. For example, the gross indication may be that 10-20% ofspaces are available, 20%-50% of spaces are available, 50%-75% of spacesare available, etc. In one embodiment, the server 205 provides real timeinformation about the level of parking space availability on nearbystreets to parking garages to allow them to dynamically tune theirprices for parking in time.

In one embodiment, the vehicle detection algorithm is based on sensingchanges in the variance of the perceived range as a vehicle drives byparked vehicles. FIG. 16A shows a raw trace 1600 of range as reported bysensor 110 in inches as a function of distance moved along a certainroad segment. FIG. 16B shows a windowed variance 1610 on the raw trace1600 of FIG. 16A, and the locations of vehicles can be determined bydetecting negative excursions on the windowed variance. Further, thewidth of the excursion (width along the x-axis) can be used to determinewhether the excursion corresponds to a single car or more than one car.This can be done by comparing the width of the excursion to a constantthat denotes the average length of a car, appropriately scaled by thespeed of the measuring vehicle at the point when the excursion occurred.

In one embodiment, the vehicle detection algorithm is based on windowedvariance and threshold detection.

Input : r(d) : Range readings r at distances d along a road segment,WindowSize, Threshold Output : Locations of parked vehicles ComputeWindowed Variance for i = 1 to length[r(d)] − WindowSize do | V (i) =variance(r(d(i) : d(i + WindowSize))) end Detect locations of vehicles:L = Locations of negative excursions of V below Threshold W = Width ofthe excursions. L, W provide the locations and number of parkedvehicles.Apart from the above windowed variance algorithm, other methods fordetecting parked vehicles include, for example, windowed mean, orwindowed mean in combination with windowed variance. In an algorithmthat uses windowed mean in combination with windowed variance, a parkedvehicle will be declared to be detected if both the windowed mean andthe windowed variance are seen to fall below pre-defined thresholds setfor the mean and variance respectively. Additionally, information aboutthe width of the excursions in the windowed mean and/or windowedvariance can be used in conjunction with the speed of the sensor vehicle(reported by the GPS receiver) to infer the number of vehicles parked ina contiguous block one after the other. In the example of FIG. 16, forinstance, the third excursion corresponds to two vehicles parked next toone another.

FIG. 17 is a block diagram illustrating an internal architecture of anexample of a computing device, such as server 205 and/or computingdevice 230, in accordance with one or more embodiments of the presentdisclosure. As shown in FIG. 17, internal architecture 1700 includes oneor more processing units (also referred to herein as CPUs) 1712, whichinterface with at least one computer bus 1702. Also interfacing withcomputer bus 1702 are persistent storage medium/media 1706, networkinterface 1714, memory 1704, e.g., random access memory (RAM), run-timetransient memory, read only memory (ROM), etc., media disk driveinterface 1708 as an interface for a drive that can read and/or write tomedia including removable media such as floppy, CD-ROM, DVD, etc. media,display interface 1710 as interface for a monitor or other displaydevice, keyboard interface 1716 as interface for a keyboard, pointingdevice interface 1718 as an interface for a mouse or other pointingdevice, and miscellaneous other interfaces not shown individually, suchas parallel and serial port interfaces, a universal serial bus (USB)interface, and the like.

Memory 1704 interfaces with computer bus 1702 so as to provideinformation stored in memory 1704 to CPU 1712 during execution ofsoftware programs such as an operating system, application programs,device drivers, and software modules that comprise program code, and/orcomputer-executable process steps, incorporating functionality describedherein, e.g., one or more of process flows described herein. CPU 1712first loads computer-executable process steps from storage, e.g., memory1704, storage medium/media 1706, removable media drive, and/or otherstorage device. CPU 1712 then can execute the stored process steps inorder to execute the loaded computer-executable process steps. Storeddata, e.g., data stored by a storage device, can be accessed by CPU 1712during the execution of computer-executable process steps.

Persistent storage medium/media 1706 is a computer readable storagemedium(s) that can be used to store software and data, e.g., anoperating system and one or more application programs. Persistentstorage medium/media 1706 also can be used to store device drivers, suchas one or more of a digital camera driver, monitor driver, printerdriver, scanner driver, or other device drivers, web pages, contentfiles, playlists and other files. Persistent storage medium/media 1706can further include program modules and data files used to implement oneor more embodiments of the present disclosure. Persistent storagemedium/media 1706 can be either remote storage or local storage incommunication with the computing device.

For the purposes of this disclosure, a computer readable storage mediumtangibly stores computer data, which data can include computer programcode executable by a computer, in machine readable form. Computerstorage media includes volatile and non-volatile, removable andnon-removable media implemented in any method or technology for storageof information such as computer-readable instructions, data structures,program modules or other data. Computer storage media includes, but isnot limited to, RAM, ROM, EPROM, EEPROM, flash memory or other solidstate memory technology, CD-ROM, DVD, or other optical storage, magneticcassettes, magnetic tape, magnetic disk storage or other magneticstorage devices, or any other medium which can be used to store thedesired information and which can be accessed by the computer.

Those skilled in the art will recognize that the methods and systems ofthe present disclosure may be implemented in many manners and as suchare not to be limited by the foregoing exemplary embodiments andexamples. In other words, functional elements being performed by singleor multiple components, in various combinations of hardware and softwareor firmware, and individual functions, may be distributed among softwareapplications at either the client or server or both. In this regard, anynumber of the features of the different embodiments described herein maybe combined into single or multiple embodiments, and alternateembodiments having fewer than, or more than, all of the featuresdescribed herein are possible. Functionality may also be, in whole or inpart, distributed among multiple components, in manners now known or tobecome known. Thus, myriad software/hardware/firmware combinations arepossible in achieving the functions, features, interfaces andpreferences described herein. Moreover, the scope of the presentdisclosure covers conventionally known manners for carrying out thedescribed features and functions and interfaces, as well as thosevariations and modifications that may be made to the hardware orsoftware or firmware components described herein as would be understoodby those skilled in the art now and hereafter.

While the system and method have been described in terms of one or moreembodiments, it is to be understood that the disclosure need not belimited to the disclosed embodiments. It is intended to cover variousmodifications and similar arrangements included within the spirit andscope of the claims, the scope of which should be accorded the broadestinterpretation so as to encompass all such modifications and similarstructures. The present disclosure includes any and all embodiments ofthe following claims.

What is claimed is:
 1. A method for obtaining parking availabilitystatistics comprising: receiving, by a server computer, sensorinformation from each of a plurality of sensors, each sensor in theplurality associated with a vehicle, the sensor information comprisinglocation coordinates of each vehicle in the plurality; and translating,by the server computer, the sensor information associated with eachvehicle in the plurality to parking statistics information.
 2. Themethod of claim 1, further comprising communicating, by the servercomputer, the parking statistics information to the vehicle.
 3. Themethod of claim 1 wherein the translating further comprises determining,by the server computer, if each vehicle in the plurality is in a slottedparking area or an unslotted parking area.
 4. The method of claim 3wherein the translating further comprises when a vehicle is in theslotted parking area, translating the sensor information associated withthe vehicle to parking space counts.
 5. The method of claim 3 whereinthe translating further comprises when a vehicle is in the unslottedparking area, translating the sensor information associated with thevehicle to a parking space map.
 6. The method of claim 1 wherein thereceiving of sensor information further comprises receiving video from acamera associated with each sensor in the plurality, the videocomprising a plurality of images that are time stamped.
 7. The method ofclaim 1 wherein the receiving further comprises determining that thelocation coordinates fall within a range of location coordinatesassociated with the start of the receiving.
 8. The method of claim 7wherein the receiving further comprises stopping the receiving when thelocation coordinates fall outside of the range of location coordinatesassociated with the start of the receiving.
 9. The method of claim 1wherein the translating further comprises determining the width of a dipin sensor information.
 10. The method of claim 9 wherein the determiningof the width further comprises determining a number of vehicles to whichthe dip corresponds.
 11. The method of claim 9 wherein the determiningof the width further comprises comparing the width to a threshold width.12. The method of claim 9 wherein the determining of the width furthercomprises determining the depth of the dip.
 13. The method of claim 1further comprising identifying a number of available parking spots bycomparing a number of cars detected with a database containing a numberof valid spots for each road.
 14. The method of claim 13 furthercomprising learning, by the server computer, the database from thesensor information by aggregating data collected from multiple passesalong the each road.
 15. The method of claim 1 wherein the translationis based on an aggregate of sensor information corresponding to theplurality of vehicles.
 16. The method of claim 1 wherein a locationestimate of the vehicle is corrected by matching time-series sensorinformation to sensor information from prior trips along a road.
 17. Themethod of claim 16 further comprising aligning traces based on distinctsignatures generated by fixed road-side objects.
 18. The method of claim1 wherein the translating is based on continually comparing time-seriessensor information observed by the each sensor to a set of signaturesknown to correspond to typical vehicles.
 19. The method of claim 1wherein the translating further comprises calculating the parkingstatistics information over a predetermined period of time.
 20. Themethod of claim 19 wherein the parking statistics information calculatedover the predetermined period of time is used to predict availability ofparking.
 21. A computer readable storage medium tangibly storingcomputer program instructions capable of being executed by a computerprocessor on a computing device, the computer program instructionsdefining the steps of: receiving sensor information from each of aplurality of sensors, each sensor in the plurality associated with avehicle, the sensor information comprising location coordinates of eachvehicle in the plurality; and translating the sensor informationassociated with each vehicle in the plurality to parking statisticsinformation.
 22. The computer readable medium of claim 21 wherein thestep of translating further comprises the step of determining if eachvehicle in the plurality is in a slotted parking area or an unslottedparking area.
 23. The computer readable medium of claim 22 wherein thestep of translating when a vehicle is in the slotted parking areafurther comprises translating the sensor information associated with thevehicle to parking space counts.
 24. The computer readable medium ofclaim 22 wherein the step of translating when a vehicle is in theunslotted parking area further comprises translating the sensorinformation associated with the vehicle to a parking space map.
 25. Thecomputer readable medium of claim 21 wherein the step of translatingfurther comprises the step of determining the width of a dip in sensorinformation.
 26. The computer readable medium of claim 25 wherein thestep of determining the width further comprises the step of determininga number of vehicles to which the dip corresponds.
 27. The computerreadable medium of claim 25 wherein the step of determining the widthfurther comprises the step of determining the depth of the dip.
 28. Thecomputer readable medium of claim 21 further comprising the step ofidentifying a number of available parking spots by comparing a number ofcars detected with a database containing a number of valid spots foreach road.
 29. The computer readable medium of claim 28 furthercomprising the step of learning the database from the sensor informationby aggregating data collected from multiple passes along the each road.30. The computer readable medium of claim of claim 21 wherein the stepof translating is based on an aggregate of sensor informationcorresponding to the plurality of vehicles.
 31. The computer readablemedium of claim 21 wherein a location estimate of the vehicle iscorrected by matching time-series sensor information to sensorinformation from prior trips along a road.
 32. The computer readablemedium of claim 31 further comprising the step of aligning traces basedon distinct signatures generated by fixed road-side objects.
 33. Thecomputer readable medium of claim of claim 21 wherein the step oftranslating is based on continually comparing time-series sensorinformation observed by the each sensor to a set of signatures known tocorrespond to typical vehicles.
 34. The computer readable medium ofclaim of claim 21 wherein the step of translating further comprisescalculating the parking statistics information over a predeterminedperiod of time.
 35. The computer readable medium of claim of claim 34wherein the parking statistics information calculated over thepredetermined period of time is used to predict availability of parking.36. A server computer for obtaining parking availability statistics, theserver computer comprising: a receiver configured to receive sensorinformation from each of a plurality of sensors, each sensor in theplurality associated with a vehicle, the sensor information comprisinglocation coordinates of each vehicle in the plurality; and a processorconfigured to translate the sensor information associated with eachvehicle in the plurality to parking statistics information.
 37. Theserver computer of claim 36, wherein the processor is further configuredto determine the width of a dip in sensor information.
 38. The servercomputer of claim 37 wherein the processor configured to determine thewidth further comprises the processor configured to determine the depthof the dip.
 39. The server computer of claim 36 wherein the processor isfurther configured to identify a number of available parking spots bycomparing a number of cars detected with a database containing a numberof valid spots for each road.
 40. The server computer of claim 39wherein the processor is further configured to learn the database fromthe sensor information by aggregating data collected from multiplepasses along the each road.
 41. The server computer of claim 36 whereinthe processor corrects a location estimate of the vehicle by matchingtime-series sensor information to sensor information from prior tripsalong a road.
 42. The server computer of claim 41 further comprising theprocessor aligning traces based on distinct signatures generated byfixed road-side objects.
 43. The server computer of claim 36 wherein theprocessor translating is based on continually comparing time-seriessensor information observed by the each sensor to a set of signaturesknown to correspond to typical vehicles.
 44. The server computer ofclaim 36 wherein the processor translating further comprises theprocessor calculating the parking statistics information over apredetermined period of time.